A substantial fraction of patients undergoing knee arthroplasty (KA) or hip arthroplasty (HA) do not achieve an improvement as high as the minimal clinically important difference (MCID), i.e. do not achieve a meaningful improvement. Using three patient-reported outcome measures (PROMs), our aim was: 1) to assess machine learning (ML), the simple pre-surgery PROM score, and logistic-regression (LR)-derived performance in their prediction of whether patients undergoing HA or KA achieve an improvement as high or higher than a calculated MCID; and 2) to test whether ML is able to outperform LR or pre-surgery PROM scores in predictive performance.

中文翻译：

---

预测患者在髋关节或膝关节置换术后是否会实现最小的临床重要差异。

相当一部分接受膝关节置换术(KA)或髋关节置换术(HA)的患者没有达到最小临床重要差异(MCID)那么高的改善，即没有实现有意义的改善。使用三种患者报告的结果测量 (PROM)，我们的目标是：1) 评估机器学习 (ML)、简单的术前 PROM 评分和逻辑回归 (LR) 预测患者是否接受手术的表现HA 或 KA 的改善程度与计算的 MCID 一样高或更高；2) 测试 ML 在预测性能方面是否能够优于 LR 或术前 PROM 分数。